Better Analysis of Defect Data at NASA
نویسندگان
چکیده
tried to explain why each cluster existed. These explanations were then assessed with the assistance of JPL mission experts. As a result of these reviews, one of the explanations was confirmed, two were rejected, and the remaining three were modified. These confirmed and modified explanations 35 Activities FlightOperations DataAccess/Delivery HardwareFailure NormalActivity Recovery SpecialProcedure
منابع مشابه
Assessing Predictors of Software Defects
OVERVIEW: When learning defect detectors from static code measures, NaiveBayes learners are better than entrophy-based decision-tree learners. Also, accuracy is not a useful way to assess those detectors. Further, those learners need no more than 200-300 examples to learn adequate detectors, especially when the data has been heavily stratified; i.e. divided up into sub-sub-sub systems (and by “...
متن کاملNew findings on the use of static code attributes for defect prediction Muhammed
Defect prediction includes tasks that are based on methods gener ated using software fault data sets and requires much effort to be completed. In defect prediction, although there are methods to conduct an analysis involving the classification of data sets and localisation of defects, those methods are not sufficient without eliminating repeated data points. The NASA Metrics Data Program (Nasa ...
متن کاملDevelopment and Validation of a Pilot Activity Load Index (PALI) based on NASA-TLX template
Abstract Introduction: Workload can be defined as the hypothetical construct that represents the cost incurred by a human operator to achieve a particular level of performance. Each job has specific needs and demands. The better measurement tool assessing that estimate the workload, it’s need to identify the requirements of a task, the circumstances under which it is performed, and the skills,...
متن کاملThe Misuse of the NASA Metrics Data Program Data Sets for Automated Software Defect Prediction
Background: The NASA Metrics Data Program data sets have been heavily used in software defect prediction experiments. Aim: To demonstrate and explain why these data sets require significant pre-processing in order to be suitable for defect prediction. Method: A meticulously documented data cleansing process involving all 13 of the original NASA data sets. Results: Post our novel data cleansing ...
متن کاملSoftware Quality Modeling with Limited Apriori Defect Data
In machine learning the problem of limited data for supervised learning is a challenging problem with practical applications. We address a similar problem in the context of software quality modeling. Knowledge-based software engineering includes the use of quantitative software quality estimation models. Such models are trained using apriori software quality knowledge in the form of software me...
متن کامل